- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Houston, TX (2018.5.14-2018.5.17)] 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Multi-core cable fault diagnosis using cluster time-frequency domain reflectometry
摘要： Guaranteeing the integrity and functionality of the control and instrumentation (C&I) cable system is essential in ensuring safe nuclear power plant (NPP) operation. When a fault occurs in a multi-core cable, it not only affects the signals of faulty lines but in fact, disturbs the rest as well due to crosstalk and noise interference. Therefore, this results in C&I signal errors in NPP operation and further leads to a rise in concern regarding the NPP operation. Thus, it is necessary for diagnostic technologies of multi-core C&I cables to classify the faulty line and detect the fault to assure the safety and reliability of NPP operation. We propose a diagnostic method that detects the fault location and faulty line in multi-core C&I cable using a clustering algorithm based on TFDR results. The faulty line detection clustering algorithm uses TFDR cross-correlation and phase synchrony results as input feature data altogether which can detect the faulty line and identify the fault point successfully. The proposed clustering algorithm is verified by experiments with two possible fault scenarios in NPP operation.
关键词： fault diagnosis,reflectometry,control and instrumentation cable,K-means clustering,crosstalk,time-frequency analysis
更新于2025-09-23 15:23:52
-
[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Joint Deep Learning and Clustering Algorithm for Liquid Particle Detection of Pharmaceutical Injection
摘要： At present, the detection of pharmaceutical injection products is a quite important step in the pharmaceutical manufacturing, as it has the direct related to the quality of medical product quality. Aiming at the difficulty that liquid particle has a smaller pixel point in the high resolution image of detection of pharmaceutical liquid particle, hence consider combined with deep neural network and clustering algorithm for detection and localization of little particle, and a processing method combining single frame images with multi-frame images was proposed to identifying liquid particle. Firstly, the single-frame image is detected by using Faster-RCNN deep neural network, and it can obtain the detection result of the 8-frame sequence image. Then hierarchical clustering and K-means clustering algorithm are used for clustering to obtain the same target motion area. In this way, liquid particle can be more accurately identified and the accuracy of detection can be greatly improved. The experimental results show that the accuracy of detection and recognition of foreign substances in liquid medicine is improved by more than 10% on average.
关键词： Liquid particle detection,Injection detection,K-means clustering,Hierarchical clustering,Faster-RCNN
更新于2025-09-23 15:22:29
-
Fluorescence Hyperspectral Imaging of Oil Samples and Its Quantitative Applications in Component Analysis and Thickness Estimation
摘要： The fast response and analysis of oil spill accidents is important but remains challenging. Here, a compact fluorescence hyperspectral system based on a grating-prism structure able to perform component analysis of oil as well as make a quantitative estimation of oil film thickness is developed. The spectrometer spectral range is 366–814 nm with a spectral resolution of 1 nm. The feasibility of the spectrometer system is demonstrated by determining the composition of three types of crude oil and various mixtures of them. The relationship between the oil film thickness and the fluorescent hyperspectral intensity is furthermore investigated and found to be linear, which demonstrates the feasibility of using the fluorescence data to quantitatively measure oil film thickness. Capable of oil identification, distribution analysis, and oil film thickness detection, the fluorescence hyperspectral imaging system presented is promising for use during oil spill accidents by mounting it on, e.g., an unmanned aerial vehicle.
关键词： K-means clustering,principal component analysis,fluorescence hyperspectral imaging,oil detection
更新于2025-09-23 15:22:29
-
GWDWT-FCM: Change Detection in SAR Images Using Adaptive Discrete Wavelet Transform with Fuzzy C-Mean Clustering
摘要： Change detection in remote sensing images turns out to play a significant role for the preceding years. Change detection in synthetic aperture radar (SAR) images comprises certain complications owing to the reality that it endures from the existence of the speckle noise. Hence, to overcome this limitation, this paper intends to develop an improved model for detecting the changes in SAR image. In this model, two SAR images captivated at varied times will be considered as the input for the change detection process. Initially, discrete wavelet transform (DWT) is employed for image fusion, where the coefficients are optimized using improved grey wolf optimization (GWO) called adaptive GWO (AGWO) algorithm. Finally, the fused images after inverse transform are clustered using fuzzy C-means (FCM) clustering technique and a similarity measure is performed among the segmented image and ground truth image. With the use of all these technologies, the proposed model is termed as adaptive grey wolf-based DWT with FCM (AGWDWT-FCM). The similarity measures analyze the relevant performance measures such as accuracy, specificity and F1 score. Moreover, the performance of the AGWDWT-FCM in change detection model is compared to other conventional models, and the improvement is noted.
关键词： Filter coefficient,Adaptive discrete wavelet transform,Grey wolf optimization,Synthetic aperture radar,Fuzzy C-means clustering
更新于2025-09-23 15:21:21
-
[Advances in Intelligent Systems and Computing] Recent Findings in Intelligent Computing Techniques Volume 709 (Proceedings of the 5th ICACNI 2017, Volume 3) || Detection and Analysis of Oil Spill in Ocean for Reduced Complexity in Extraction Using Image Processing
摘要： Oil spills occurring in oceans are difficult to detect and require sophisticated measures to obtain and analyze the images. In this chapter, both color image using high-resolution cameras and Synthetic Aperture Radar (SAR) images are analyzed and certain useful results are obtained to reduce the complexity in extracting the oil spills. The recognition and examination of the oil spill images are done using image processing technique. Furthermore, if the oil spill is scattered as patches, the algorithm classifies the patches into smaller patches and larger ones by using k-means clustering. Hence, the patches depending on the size or intensity can be extracted on a simpler basis.
关键词： Image processing,Synthetic aperture radar (SAR) images,Machine learning,K-means clustering
更新于2025-09-23 15:21:01
-
Decoupling mesoscale functional response in PLZT across the ferroelectric – relaxor phase transition with contact Kelvin probe force microscopy and machine learning
摘要： Relaxor ferroelectrics exhibit a range of interesting material behavior including high electromechanical response, polarization rotations as well as temperature and electric field-driven phase transitions. The origin of this unusual functional behavior remains elusive due to limited knowledge on polarization dynamics at the nanoscale. Piezoresponse force microscopy and associated switching spectroscopy provide access to local electromechanical properties on the micro- and nanoscale, which can help to address some of these gaps in our knowledge. However, these techniques are inherently prone to artefacts caused by signal contributions emanating from electrostatic interactions between tip and sample. Understanding functional behavior of complex, disordered systems like relaxor materials with unknown electromechanical properties therefore requires a technique that allows to distinguish between electromechanical and electrostatic response. Here, contact Kelvin probe force microscopy (cKPFM) is used to gain insight into the evolution of local electromechanical and capacitive properties of a representative relaxor material lead lanthanum zirconate across the phase transition from a ferroelectric to relaxor state. The obtained multidimensional data set was processed using an unsupervised machine learning algorithm to detect variations in functional response across the probed area and temperature range. Further analysis showed formation of two separate cKPFM response bands below 50°C, providing evidence for polarization switching. At higher temperatures only one band is observed, indicating an electrostatic origin of the measured response. In addition, from the cKPFM data qualitatively extracted junction potential difference, becomes independent of the temperature in the relaxor state. The combination of this multidimensional voltage spectroscopy technique and machine learning allows to identify the origin of the measured functional response and to decouple ferroelectric from electrostatic phenomena necessary to understand the functional behavior of complex, disordered systems like relaxor materials.
关键词： phase transition,machine learning,Relaxor ferroelectric,lead lanthanum zirconium titanate,piezoresponse force microscopy,k-means clustering,contact Kelvin probe force microscopy
更新于2025-09-23 15:21:01
-
Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry
摘要： The contribution and impact of combined laser ablation inductively coupled plasma time of flight mass spectrometry (LA-ICP-TOF-MS) and laser induced breakdown spectroscopy (LIBS) were evaluated for the discrimination analysis of different coal samples. This Tandem approach allows simultaneous determination of major and minor elements (C, H, Si, Ca, Al, Mg, etc), and trace elements (V, Ba, Pb, U, etc.) in the coal. The research focused on coal classification strategies based on principle component analysis (PCA) combined with K-means clustering, partial least squares discrimination analysis (PLS-DA), and support vector machine (SVM) for analytical performance. Correlation analyses performed from TOF mass and LIBS emission spectra from the coal samples showed that most major, minor, and trace elements emissions had negative correlation with the volatile content. Suitable variables for the classification models were determined from these data. The individual TOF data, LIBS data, and the combined data of TOF and LIBS, respectively, as the input for different models were analyzed and compared. In all cases, the results obtained with the combined TOF and LIBS data were found to be superior to those obtained with the individual TOF or LIBS data. The nonlinear SVM model combined with TOF and LIBS data provided the best coal classification performance, with a classification accuracy of up to 98%.
关键词： Principal component analysis,Support vector machine,Partial least squares discrimination analysis,Laser-induced breakdown spectroscopy,K-means clustering,Coal discrimination,Laser ablation inductively coupled plasma time of flight mass spectrometry
更新于2025-09-23 15:19:57
-
Uniform Star Catalogue using GWKM Clustering for Application in Star Sensors
摘要： In this paper, a novel algorithm of weighted k-means clustering with geodesic criteria is presented to generate a uniform database for a star sensor. For this purpose, selecting the appropriate star catalogue and desirable minimum magnitude and eliminating double stars are among the steps of the uniformity process. Further, Delaunay triangulation and determining the scattered data density by using a Voronoi diagram were used to solve the problems of the proposed clustering method. Thus, by running a Monte Carlo simulation to count the number of stars observed in different fields of view, it was found that the uniformity leads to a significant reduction of the probability of observing a large number of stars in all fields of view. In contrast, the uniformity slightly increased the field of view needed to observe the minimum number of required stars for an identification algorithm.
关键词： Geodesic k-means clustering,Scattered data density,Delaunay triangulation,Optimized star catalogue
更新于2025-09-19 17:15:36
-
Identification of tea varieties by mid‐infrared diffuse reflectance spectroscopy coupled with a possibilistic fuzzy c‐means clustering with a fuzzy covariance matrix
摘要： Mid-infrared diffuse reflectance spectroscopy was used to rapidly and nondestructively identify tea varieties together with the proposed possibilistic fuzzy c-means (PFCM) clustering with a fuzzy covariance matrix. The mid-infrared diffuse reflectance spectra of 96 tea samples with three different varieties (Emeishan Maofeng, Level 1, and Level 6 Leshan trimeresurus) were acquired using the FTIR-7600 infrared spectrometer. First, multiplicative scatter correction was implemented to pretreat the spectral data. Second, principal component analysis was employed to compress the mid-infrared diffuse reflectance spectral data after preprocessing. Third, linear discriminant analysis was utilized for extracting the identification information required by the fuzzy clustering algorithms. Ultimately, the fuzzy c-means (FCM) clustering, the allied fuzzy c-means (AFCM) clustering, the PFCM clustering, and the PFCM clustering with a fuzzy covariance matrix were used to cluster the processed spectral data, respectively. The highest identification accuracy of the PFCM clustering with a fuzzy covariance matrix reached at 100% compared with those of FCM (96.7%), AFCM (94.9%), PFCM (96.3%), and partial least squares discrimination analysis (PLS-DA) algorithm (33.3%). It is sufficiently demonstrated that the mid-infrared diffuse reflectance spectroscopy coupled with the PFCM clustering with a fuzzy covariance matrix was a valid method for identifying tea varieties.
关键词： possibilistic fuzzy c-means clustering,tea varieties,Mid-infrared diffuse reflectance spectroscopy,fuzzy covariance matrix,nondestructive detection
更新于2025-09-11 14:15:04
-
Location Ambiguity Resolution and Tracking Method of Human Targets in Wireless Infrared Sensor Network
摘要： Human tracking has attracted extensive attention by using low-cost pyroelectric infrared sensor network in recent years. This paper presents a location ambiguity resolution and tracking method for human targets in wireless, distributed and binary infrared sensor network. The tracking system can detect the human targets in the detection space, and activate the sensor detection lines dynamically. A bearing-crossing location method is designed. The intersections of all activated detection lines are called primary measurement points for human location, and some of them are false measurement points. The ambiguity of this bearing-crossing location method is discussed and a two-level bearing-crossing algorithm is proposed based on quartic K-means clustering and joint cost function. For the first level, an anti-logic algorithm is designed to get the initial effective measurement points, then these points are assigned to different targets using K-means clustering. For the second level, the final effective points are obtained by using a special joint cost function, and they are assigned to different targets using K-means clustering once again to get the final locating results. The cost value is used as a weight to adjust the covariance parameter in Kalman filter for target tracking as well. The experimental results show that the average tracking error of human targets is less than 0.8 m in a 10 m×10 m space, which verify the proposed location ambiguity resolution and tracking method.
关键词： Wireless Infrared sensor network,Cost function,Multiple human tracking,Binary pyroelectric infrared sensor network,Location ambiguity,Bearing-crossing location,Quadratic K-means clustering
更新于2025-09-10 09:29:36